System identification
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Fault Diagnosis: Models, Artificial Intelligence, Applications
Fault Diagnosis: Models, Artificial Intelligence, Applications
Diagnosis and Fault-Tolerant Control
Diagnosis and Fault-Tolerant Control
Artificial intelligence for monitoring and supervisory control of process systems
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Online fault detection and isolation of nonlinear systems based on neurofuzzy networks
Engineering Applications of Artificial Intelligence
Confidence estimation of the multi-layer perceptron and its application in fault detection systems
Engineering Applications of Artificial Intelligence
A new approach to intelligent fault diagnosis of rotating machinery
Expert Systems with Applications: An International Journal
Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems: From Analytical to Soft Computing Approaches
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The increased complexity of plants and the development of sophisticated control systems have encouraged the parallel development of efficient rapid fault detection and isolation (FDI) systems. FDI in industrial system has lately become of great significance. This paper proposes a new technique for short time fault detection and diagnosis in nonlinear dynamic systems with multi inputs and multi outputs. The main contribution of this paper is to develop a FDI schema according to reference models of fault-free and faulty behaviors designed with neural networks. Fault detection is obtained according to residuals that result from the comparison of measured signals with the outputs of the fault free reference model. Then, Euclidean distance from the outputs of models of faults to themeasurements leads to fault isolation. The advantage of this method is to provide not only early detection but also early diagnosis thanks to the parallel computation of the models of faults and to the proposed decision algorithm. The effectiveness of this approach is illustrated with simulations on DAMADICS benchmark.